To learn each game, an untrained neural network plays millions of games against itself via a process of trial and error called reinforcement learning. Note: each training step represents 4,096 board positions. In chess, AlphaZero first outperformed Stockfish after just 4 hours in shogi, AlphaZero first outperformed Elmo after 2 hours and in Go, AlphaZero first outperformed the version of AlphaGo that beat the legendary player Lee Sedol in 2016 after 30 hours. Chess Grandmaster Matthew Sadler and Women’s International Master Natasha Regan, who have analysed thousands of AlphaZero’s chess games for their forthcoming book Game Changer (New in Chess, January 2019), say its style is unlike any traditional chess engine.” It’s like discovering the secret notebooks of some great player from the past,” says Matthew. This ability to learn each game afresh, unconstrained by the norms of human play, results in a distinctive, unorthodox, yet creative and dynamic playing style. I can’t disguise my satisfaction that it plays with a very dynamic style, much like my own!' Garry Kasparov, Former World Chess Champion It describes how AlphaZero quickly learns each game to become the strongest player in history for each, despite starting its training from random play, with no in-built domain knowledge but the basic rules of the game.

Today, we are delighted to introduce the full evaluation of AlphaZero, published in the journal Science ( Open Access version here), that confirms and updates those preliminary results. We were excited by the preliminary results and thrilled to see the response from members of the chess community, who saw in AlphaZero’s games a ground-breaking, highly dynamic and “ unconventional ” style of play that differed from any chess playing engine that came before it.

In late 2017 we introduced AlphaZero, a single system that taught itself from scratch how to master the games of chess, shogi (Japanese chess), and Go, beating a world-champion program in each case.

Game changer alphazero